GIS and Crime Analysis

Spatial analysis of crime has gained increasing attention during the past thirty years, coupled with the growth of geographic information systems (GIS). Most crime analysis tasks are either carried out in a GIS environment or supported by a GIS. GIS is typically used as a tool for data management, data processing, data visualization, and data analysis for crime studies. Crime analysis normally involves the following elements: uncovering spatio-temporal patterns of crime distribution, such as crime hotspots; explaining these patterns and discerning major contributing factors based on multivariate regression modeling; predicting future crime patterns using machine learning and other predictive methods; developing crime prevention approaches based on historical and future crime patterns; and evaluating the effectiveness of crime prevention, to find out if crime is reduced in the targeted area and whether the nearby areas are affected by the intervention. It should be noted that crime analysis is inherently multidisciplinary, including but not limited to geography, criminology, computer science, statistics, urban planning, and sociology. Therefore, an effective crime analyst should be well trained in multiple disciplinary approaches. Any crime analysis that leads to real-world impact must rely on sound theories and effective methodologies. Many of the theories covered in this article are related to geography, criminology, and sociology. The methods are mostly influenced by GIS, spatial statistics, and artificial intelligence. Crime analysis also involves multiple stakeholders, including at least government agencies, universities, and private companies. Universities conduct basic and applied research, private companies convert the research to products, and government agencies provide funding for research and implement crime prevention strategies. In addition, crime analysis needs to pay close attention to potential issues related to ethics, privacy, confidentiality, and discrimination.

Crime and COVID-19: effect of changes in routine activities in Mexico City

Background This study aimed to determine whether crime patterns in Mexico City changed due to the COVID-19 pandemic, and to test whether any changes observed were associated with the disruption of routine activities, as measured by changes in public transport passenger numbers. Method The first objective was assessed by comparing the observed incidence of crime after the COVID-19 pandemic was detected in the country with that expected based on ARIMA forecasts based on the pre-pandemic trends. The second objective was assessed by examining the association between crime incidence and the number of passengers on public transport using regressions with ARIMA errors. Results Results indicated that most crime categories decreased significantly after the pandemic was detected in the country or after a national lockdown was instituted. Furthermore, the study found that some of the declines observed were associated with the reductions seen in public transport passenger numbers. However, the findings suggested that the changes in mobility explain part of the declines observed, with important variations per crime type. Conclusion The findings contribute to the global evaluation of the effects of COVID-19 on crime and propose a robust method to explicitly test whether the changes observed are associated with changes in routine activities.